【摘要】 本文以珠江三角洲地震监测和预警系统粤东密集台网的强震日常记录数据为基础,利用强震台站背景噪声有效值密度函数,研究强震动台站背景噪声频谱的统计规律,建立强震台站背景噪声有效值平均模型、最小模型以及噪声有效值概率分布区间,通过利用强震台站背景噪声有效值概率分布区间与区域地震事件的频率-加速度幅值分布曲线互比的强震台站监测能力分析方法,得到了不同台站每日背景噪声加速度有效值,并估算了强震台站记录不同震级区域地震事件的概率,来评价台站的监测能力。强震台站背景噪声有效值概率分布区间分析方法是背景噪声有效值概率密度分布分析的延伸和拓展,有助于工程地震学中频率域去噪低端截止频率的讨论。由于仪器自噪声和环境噪声的相互作用不同而导致台站的噪声下限不同,强震台站背景噪声最小模型可以作为该台最优监测能力的估计,是强震仪及观测环境的综合指标。更多还原

【Abstract】 This study is based on the strong motion daily records from the dense network of seismic monitoring and early warning systems of the Pearl River Delta in eastern Guangdong.By utilizing the RMS density function of background noise at strong motion stations,we investigate the statistical characteristics of the background noise spectrum at these stations. We establish the average model,minimum model,and probability distribution interval for the RMS of background noise. This forms the basis for a method to analyze the monitoring capabilities of strong motion stations by comparing the probability distribution intervals of background noise RMS with the frequency-acceleration amplitude distribution curves of regional earthquake events.Using this method,we obtain the daily background noise acceleration RMS for different stations and estimate the probability of recording regional seismic events of various magnitudes,thereby evaluating the monitoring capabilities of the stations. The lower noise limits for different stations vary due to the interaction between instrument self-noise and environmental noise.The minimum model of background noise RMS can be used as an estimate of the optimal monitoring capability of a station,serving as a comprehensive indicator of both the strong motion instrument and the observation environment. This also contributes to discussions on the low-end cut-off frequency for denoising in the frequency domain within engineering seismology. The probability distribution interval analysis method for background noise RMS is an extension and expansion of the probability density distribution analysis of background noise RMS.更多还原